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  <h1>Source code for torch.distributed.optim.optimizer</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="k">as</span> <span class="nn">dist_autograd</span>

<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">from</span> <span class="nn">threading</span> <span class="kn">import</span> <span class="n">Lock</span>


<span class="k">class</span> <span class="nc">_LocalOptimizer</span><span class="p">:</span>
    <span class="c1"># Ideally we would only need to share a lock for instances of</span>
    <span class="c1"># _LocalOptimizer that deal with the same parameters. We are</span>
    <span class="c1"># making a simplifying assumption here that if there is more</span>
    <span class="c1"># than one instance of _LocalOptimizer per worker, they will</span>
    <span class="c1"># be optimizing the same parameters (e.g. each data parallel</span>
    <span class="c1"># trainer will create its own instance of _LocalOptimizer but</span>
    <span class="c1"># they will all optimize the same parameters on each worker)</span>
    <span class="n">global_lock</span> <span class="o">=</span> <span class="n">Lock</span><span class="p">()</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">optim</span> <span class="o">=</span> <span class="n">optim_cls</span><span class="p">(</span>
            <span class="p">[</span><span class="n">rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span> <span class="k">for</span> <span class="n">rref</span> <span class="ow">in</span> <span class="n">local_params_rref</span><span class="p">],</span>
            <span class="o">*</span><span class="n">args</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">autograd_ctx_id</span><span class="p">):</span>
        <span class="n">all_local_grads</span> <span class="o">=</span> <span class="n">dist_autograd</span><span class="o">.</span><span class="n">get_gradients</span><span class="p">(</span><span class="n">autograd_ctx_id</span><span class="p">)</span>

        <span class="k">with</span> <span class="n">_LocalOptimizer</span><span class="o">.</span><span class="n">global_lock</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">param</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="n">all_local_grads</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="n">param</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>


<span class="k">def</span> <span class="nf">_new_local_optimizer</span><span class="p">(</span><span class="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">rpc</span><span class="o">.</span><span class="n">RRef</span><span class="p">(</span>
        <span class="n">_LocalOptimizer</span><span class="p">(</span><span class="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>


<span class="k">def</span> <span class="nf">_local_optimizer_step</span><span class="p">(</span><span class="n">local_optim_rref</span><span class="p">,</span> <span class="n">autograd_ctx_id</span><span class="p">):</span>
    <span class="n">local_optim</span> <span class="o">=</span> <span class="n">local_optim_rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span>
    <span class="n">local_optim</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">autograd_ctx_id</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_wait_for_all</span><span class="p">(</span><span class="n">rpc_futs</span><span class="p">):</span>
    <span class="c1"># TODO: improve error propagation</span>
    <span class="n">exception</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">fut</span> <span class="ow">in</span> <span class="n">rpc_futs</span><span class="p">:</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fut</span><span class="o">.</span><span class="n">wait</span><span class="p">())</span>
        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
            <span class="n">exception</span> <span class="o">=</span> <span class="n">e</span>
    <span class="k">if</span> <span class="n">exception</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="n">exception</span>
    <span class="k">return</span> <span class="n">results</span>


<div class="viewcode-block" id="DistributedOptimizer"><a class="viewcode-back" href="../../../../rpc/rpc.html#torch.distributed.optim.DistributedOptimizer">[docs]</a><span class="k">class</span> <span class="nc">DistributedOptimizer</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    DistributedOptimizer takes remote references to parameters scattered</span>
<span class="sd">    across workers and applies the given optimizer locally for each parameter.</span>

<span class="sd">    This class uses :meth:`~torch.distributed.autograd.get_gradients` in order</span>
<span class="sd">    to retrieve the gradients for specific parameters.</span>

<span class="sd">    Concurrent calls to</span>
<span class="sd">    :meth:`~torch.distributed.optim.DistributedOptimizer.step`,</span>
<span class="sd">    either from the same or different clients, will</span>
<span class="sd">    be serialized on each worker -- as each worker&#39;s optimizer can only work</span>
<span class="sd">    on one set of gradients at a time. However, there is no guarantee that</span>
<span class="sd">    the full forward-backward-optimizer sequence will execute for one client</span>
<span class="sd">    at a time. This means that the gradients being applied may not correspond</span>
<span class="sd">    to the latest forward pass executed on a given worker. Also, there is no</span>
<span class="sd">    guaranteed ordering across workers.</span>

<span class="sd">    Args:</span>
<span class="sd">        optimizer_class (optim.Optimizer): the class of optimizer to</span>
<span class="sd">            instantiate on each worker.</span>
<span class="sd">        params_rref (list[RRef]): list of RRefs to local or remote parameters</span>
<span class="sd">            to optimize.</span>
<span class="sd">        args: arguments to pass to the optimizer constructor on each worker.</span>
<span class="sd">        kwargs: arguments to pass to the optimizer constructor on each worker.</span>

<span class="sd">    Example::</span>
<span class="sd">        &gt;&gt;&gt; import torch.distributed.autograd as dist_autograd</span>
<span class="sd">        &gt;&gt;&gt; import torch.distributed.rpc as rpc</span>
<span class="sd">        &gt;&gt;&gt; from torch import optim</span>
<span class="sd">        &gt;&gt;&gt; from torch.distributed.optim import DistributedOptimizer</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; with dist_autograd.context() as context_id:</span>
<span class="sd">        &gt;&gt;&gt;   # Forward pass.</span>
<span class="sd">        &gt;&gt;&gt;   rref1 = rpc.remote(&quot;worker1&quot;, torch.add, args=(torch.ones(2), 3))</span>
<span class="sd">        &gt;&gt;&gt;   rref2 = rpc.remote(&quot;worker1&quot;, torch.add, args=(torch.ones(2), 1))</span>
<span class="sd">        &gt;&gt;&gt;   loss = rref1.to_here() + rref2.to_here()</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt;   # Backward pass.</span>
<span class="sd">        &gt;&gt;&gt;   dist_autograd.backward(context_id, [loss.sum()])</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt;   # Optimizer.</span>
<span class="sd">        &gt;&gt;&gt;   dist_optim = DistributedOptimizer(</span>
<span class="sd">        &gt;&gt;&gt;      optim.SGD,</span>
<span class="sd">        &gt;&gt;&gt;      [rref1, rref2],</span>
<span class="sd">        &gt;&gt;&gt;      lr=0.05,</span>
<span class="sd">        &gt;&gt;&gt;   )</span>
<span class="sd">        &gt;&gt;&gt;   dist_optim.step(context_id)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">optimizer_class</span><span class="p">,</span> <span class="n">params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="n">per_worker_params_rref</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">params_rref</span><span class="p">:</span>
            <span class="n">per_worker_params_rref</span><span class="p">[</span><span class="n">param</span><span class="o">.</span><span class="n">owner</span><span class="p">()]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>

        <span class="n">remote_optim_futs</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">worker</span><span class="p">,</span> <span class="n">param_rrefs</span> <span class="ow">in</span> <span class="n">per_worker_params_rref</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">remote_optim_rref_fut</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span>
                <span class="n">worker</span><span class="p">,</span>
                <span class="n">_new_local_optimizer</span><span class="p">,</span>
                <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">optimizer_class</span><span class="p">,</span> <span class="n">param_rrefs</span><span class="p">)</span> <span class="o">+</span> <span class="n">args</span><span class="p">,</span>
                <span class="n">kwargs</span><span class="o">=</span><span class="n">kwargs</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="n">remote_optim_futs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">remote_optim_rref_fut</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">remote_optimizers</span> <span class="o">=</span> <span class="n">_wait_for_all</span><span class="p">(</span><span class="n">remote_optim_futs</span><span class="p">)</span>

<div class="viewcode-block" id="DistributedOptimizer.step"><a class="viewcode-back" href="../../../../rpc/rpc.html#torch.distributed.optim.DistributedOptimizer.step">[docs]</a>    <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context_id</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Performs a single optimization step.</span>

<span class="sd">        This will call :meth:`torch.optim.Optimizer.step` on each worker</span>
<span class="sd">        containing parameters to be optimized, and will block until all workers</span>
<span class="sd">        return. The provided ``context_id`` will be used to retrieve the</span>
<span class="sd">        corresponding :class:`~torch.distributed.autograd.context` that</span>
<span class="sd">        contains the gradients that should be applied to the parameters.</span>

<span class="sd">        Args:</span>
<span class="sd">            context_id: the autograd context id for which we should run the</span>
<span class="sd">                optimizer step.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">dist_autograd</span><span class="o">.</span><span class="n">_is_valid_context</span><span class="p">(</span><span class="n">context_id</span><span class="p">)</span>
        <span class="n">rpc_futs</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">optim</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">remote_optimizers</span><span class="p">:</span>
            <span class="n">rpc_futs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span>
                <span class="n">optim</span><span class="o">.</span><span class="n">owner</span><span class="p">(),</span>
                <span class="n">_local_optimizer_step</span><span class="p">,</span>
                <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">optim</span><span class="p">,</span> <span class="n">context_id</span><span class="p">),</span>
            <span class="p">))</span>
        <span class="n">_wait_for_all</span><span class="p">(</span><span class="n">rpc_futs</span><span class="p">)</span></div></div>
</pre></div>

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